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A novel architecture is proposed that addresses sample efficiency in VLAs by training a predictive world model on the embedding space of the VLA's vision encoder, and it is hypothesized that these embeddings are action-relevant and usable for future prediction.
Vision-language-action models often predict actions from only the current observation, which can leave tasks involving object occlusion or visually identical objects ambiguous without episode history. The usual countermeasure, widening the observation window, turns the horizon into a hyperparameter and lets per-step co...
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